Heart Disease Prediction Using Machine Learning: A Comparative Analysis of Classification Algorithms

Sayama Alam

doi:10.36948/ijfmr.2026.v08i04.82813

Heart Disease Prediction Using Machine Learning: A Comparative Analysis of Classification Algorithms

Author(s)	Ms. Sayama Alam
Country	Bangladesh
Abstract	Abstract Heart disease is a general term that includes many types of heart problems. It is also called cardiovascular disease which means heart and blood vessel disease. Now a day’s heart disease is one of the most prominent causes of mortality worldwide. According to estimates from the World Health Organization, heart disease causes 12 million deaths annually worldwide. Cardiovascular diseases account for half of all deaths in the US and other affluent nations. With the advent of new web technology, analysis and prediction of heart disease has paved the way of invoking an efficient and effective research era. Improving patient outcomes and lowering death rates depend on early identification of cardiac disease. To automatically predict, diagnose, and treat heart disease, machine learning (ML) algorithms and techniques have been applied to a variety of available heart disease datasets during the past several years. By using massive volumes of patient data to find patterns that might not be immediately noticeable to medical practitioners, machine learning (ML) offers potential methods for the early identification and prediction of cardiac disease. Logistic Regression, Decision Trees, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest, Gradient Boosting, Naive Bayes, XGBoost and AdaBoost are the nine machine learning models that have been chosen for evaluation. The models' effectiveness was evaluated based on accuracy, F1-score, and area under the ROC curve (AUC). According to the results, most of the models gave satisfactory results. These results demonstrate how machine learning-based methods can increase the precision of diagnoses, enable early treatments, and support medical practitioners in making well-informed decisions about the management and treatment of cardiac disease.
Keywords	Heart disease, heart disease prediction, machine learning models, models evaluation, classification
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-07-02
DOI	https://doi.org/10.36948/ijfmr.2026.v08i04.82813

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Heart Disease Prediction Using Machine Learning: A Comparative Analysis of Classification Algorithms

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